Vision-Servoed Localization and Behavior-Based Planning for an Autonomous Quadruped Legged Robot

Planning actions for real robots in dynamic and uncertain environments is a challenging problem. It is not viable to use a complete model of the world; it is most appropriate to achieve goals and handle uncertainty by integrating deliberation and behavior-based reactive planning. We successfully developed a system integrating perception and action for the RoboCup-99 Sony legged robot league. The quadruped legged robots are fully autonomous and thus must have onboard vision, localization and action selection. We briefly present our perception algorithm that automatically classifies and tracks colored blobs in real time. We then briefly introduce our Sensor Resetting Localization (SRL) algorithm which is an extension of Monte Carlo Localization. Vision and localization provide the state input for action selection. Our robust and sensible behavior scheme handles dynamic changes in information accuracy. We developed a utility-based system for using and acquiring location information. Finally, we have devised several special built-in plans to deal with times when urgent action is needed and the robot cannot afford to collect accurate location information. We present results using t he real robots, which demonstrate the success of our approach. Our team of Sony quadruped legged robots, CMTrio-99, won all but one of its games in RoboCup-99, and was awarded third place in the competition.